We present a new and improved version (V4.0) of the NASA standard nitrogen dioxide (NO₂) product from the Ozone Monitoring Instrument (OMI) on the Aura satellite. This version incorporates the most salient improvements for OMI NO₂ products suggested by expert users and enhances the NO₂ data quality in several ways through improvements to the air mass factors (AMFs) used in the retrieval algorithm. The algorithm is based on the geometry-dependent surface Lambertian equivalent reflectivity (GLER) operational product that is available on an OMI pixel basis. GLER is calculated using the vector linearized discrete ordinate radiative transfer (VLIDORT) model, which uses as input high-resolution bidirectional reflectance distribution function (BRDF) information from NASA's Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) instruments over land and the wind-dependent Cox–Munk wave-facet slope distribution over water, the latter with a contribution from the water-leaving radiance. The GLER combined with consistently retrieved oxygen dimer (O₂–O₂) absorption-based effective cloud fraction (ECF) and optical centroid pressure (OCP) provide improved information to the new NO₂ AMF calculations. The new AMFs increase the retrieved tropospheric NO₂ by up to 50 % in highly polluted areas; these differences arise from both cloud and surface BRDF effects as well as biases between the new MODIS-based and previously used OMI-based climatological surface reflectance data sets. We quantitatively evaluate the new NO₂ product using independent observations from ground-based and airborne instruments. The new V4.0 data and relevant explanatory documentation are publicly available from the NASA Goddard Earth Sciences Data and Information Services Center (https://disc.gsfc.nasa.gov/datasets/OMNO2_V003/summary/, last access: 8 November 2020), and we encourage their use over previous versions of OMI NO₂ products.

The Dutch–Finnish Ozone Monitoring Instrument (OMI) is an imaging spectrograph flying on NASA's EOS Aura satellite since 15 July 2004. OMI is primarily used to map trace-gas concentrations in the Earth's atmosphere, obtaining mid-resolution (0.4–0.6 nm) ultraviolet–visible (UV–VIS; 264–504 nm) spectra at multiple (30–60) simultaneous fields of view. Assessed via various approaches that include monitoring of radiances from selected ocean, land ice and cloud areas, as well as measurements of line profiles in the solar spectra, the instrument shows low optical degradation and high wavelength stability over the mission lifetime. In the regions relatively free from the slowly unraveling row anomaly (RA) the OMI irradiances have degraded by 3–8 %, while radiances have changed by 1–2 %. The long-term wavelength calibration of the instrument remains stable to 0.005–0.020 nm.

Under a NASA program to produce long‐term data records from instruments on multiple satellites, data from a series of nine Solar Backscatter Ultraviolet (SBUV and SBUV/2) instruments have been reprocessed to create a coherent ozone time series. Data from the BUV instrument on Nimbus 4, SBUV on Nimbus 7, and SBUV/2 instruments on NOAA 9, 11, 14, 16, 17, 18, and 19 covering the period 1970–1972 and 1979–2011 were used to create a long‐term data set. The goal is an ozone Earth Science Data Record—a consistent, calibrated ozone time series that can be used for trend analyses and other studies. In order to create this ozone data set, the radiances were adjusted and used to reprocess the entire data records for each of the nine instruments. Interinstrument comparisons during periods of overlap as well as comparisons with data from other satellite and ground‐based instruments were used to evaluate the consistency of the record and make calibration adjustments as needed. Additional improvements in this version 8.6 processing included the use of the Brion, Daumont, and Malicet ozone cross sections, and a cloud‐height climatology derived from Aura OMI measurements. Validation of the reprocessed ozone shows that total column ozone is consistent with the Brewer/Dobson network to within about 1% for the new time series. Comparisons with MLS, SAGE, sondes, and lidar show that ozone at individual levels in the stratosphere is generally consistent to within 5%.

NASA satellite measurements show that ozone reductions throughout the Northern Hemisphere (NH) free troposphere reported for spring‐summer 2020 during the COronaVIrus Disease 2019 pandemic have occurred again in spring‐summer 2021. The satellite measurements show that tropospheric column ozone (TCO) (mostly representative of the free troposphere) for 20°N–60°N during spring‐summer for both 2020 and 2021 averaged ∼3 Dobson Units (DU) (or ∼7%–8%) below normal. These ozone reductions in 2020 and 2021 were the lowest in the 2005–2021 record. We also include satellite measurements of tropospheric NO₂ that exhibit reductions of ∼10%–20% in the NH in early spring‐to‐summer 2020 and 2021, suggesting that reduced pollution was the main cause for the low anomalies in NH TCO in 2020 and 2021. Reductions of TCO ∼2 DU (7%) are also measured in the Southern Hemisphere in austral summer but are not associated with reduced NO₂.

Most satellite nadir ultraviolet and visible cloud, aerosol, and trace-gas algorithms make use of climatological surface reflectivity databases. For example, cloud and NO₂ retrievals for the Ozone Monitoring Instrument (OMI) use monthly gridded surface reflectivity climatologies that do not depend upon the observation geometry. In reality, reflection of incoming direct and diffuse solar light from land or ocean surfaces is sensitive to the sun–sensor geometry. This dependence is described by the bidirectional reflectance distribution function (BRDF). To account for the BRDF, we propose to use a new concept of geometry-dependent Lambertian equivalent reflectivity (LER). Implementation within the existing OMI cloud and NO₂ retrieval infrastructure requires changes only to the input surface reflectivity database. The geometry-dependent LER is calculated using a vector radiative transfer model with high spatial resolution BRDF information from the Moderate Resolution Imaging Spectroradiometer (MODIS) over land and the Cox–Munk slope distribution over ocean with a contribution from water-leaving radiance. We compare the geometry-dependent and climatological LERs for two wavelengths, 354 and 466 nm, that are used in OMI cloud algorithms to derive cloud fractions. A detailed comparison of the cloud fractions and pressures derived with climatological and geometry-dependent LERs is carried out. Geometry-dependent LER and corresponding retrieved cloud products are then used as inputs to our OMI NO₂ algorithm. We find that replacing the climatological OMI-based LERs with geometry-dependent LERs can increase NO₂ vertical columns by up to 50 % in highly polluted areas; the differences include both BRDF effects and biases between the MODIS and OMI-based surface reflectance data sets. Only minor changes to NO₂ columns (within 5 %) are found over unpolluted and overcast areas.

This paper verifies and corrects the Ozone Mapping and Profiler Suite (OMPS) nadir mapper (NM) level 1B v2.0 measurements with the aim of producing accurate ozone profile retrievals using an optimal-estimation-based inversion method to fit measurements in the spectral range 302.5–340 nm. The evaluation of available slit functions demonstrates that preflight-measured slit functions represent OMPS measurements well compared to derived Gaussian slit functions. Our initial OMPS fitting residuals contain significant wavelength and cross-track-dependent biases, resulting in serious cross-track striping errors in the tropospheric ozone retrievals. To eliminate the systematic component of the fitting residuals, we apply soft calibration to OMPS radiances. With the soft calibration the amplitude of fitting residuals decreases from ∼ 1 to 0.2 % over low and middle latitudes, and thereby the consistency of tropospheric ozone retrievals between OMPS and the Ozone Monitoring Instrument (OMI) is substantially improved. A common mode correction is also implemented for additional radiometric calibration; it improves retrievals especially at high latitudes where the amplitude of fitting residuals decreases by a factor of ∼ 2. We estimate the noise floor error of OMPS measurements from standard deviations of the fitting residuals. The derived error in the Huggins band ( ∼ 0.1 %) is twice the OMPS L1B measurement error. OMPS noise floor errors constrain our retrievals better, leading to improving information content of ozone and reducing fitting residuals. The final precision of the fitting residuals is less than 0.1 % in the low and middle latitudes, with ∼ 1 degrees of freedom for signal for the tropospheric ozone, meeting the general requirements for successful tropospheric ozone retrievals.